{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/39357"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/39357","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"A Quaternion-Based Motion Tracking and Gesture Recognition System Using Wireless Inertial Sensors","abstract":"This work examines the development of a unified motion tracking and gesture recognition system that functions through worn inertial sensors. The system is comprised of a total of ten sensors and uses their quaternion output to map the player's motions to an onscreen character. To demonstrate the system's capabilities, a simple virtual reality game was created. A hierarchical skeletal model was implemented that allows players to navigate the virtual world without the need of a hand-held controller. In addition to motion tracking, the system was tested for its potential for gesture recognition. Despite the widespread use of Hidden Markov Models, our modified Markov Chain algorithm obtained higher average recognition accuracies at 95% and faster computation times. Combining motion tracking and dynamic gesture recognition into a single unified system is unique in the literature and comes at a time when virtual reality and wearable computing are emerging in the marketplace.","abstract_html":"This work examines the development of a unified motion tracking and gesture recognition system that functions through worn inertial sensors. The system is comprised of a total of ten sensors and uses their quaternion output to map the player's motions to an onscreen character. To demonstrate the system's capabilities, a simple virtual reality game was created. A hierarchical skeletal model was implemented that allows players to navigate the virtual world without the need of a hand-held controller. In addition to motion tracking, the system was tested for its potential for gesture recognition. Despite the widespread use of Hidden Markov Models, our modified Markov Chain algorithm obtained higher average recognition accuracies at 95% and faster computation times. 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